TL;DR
This thesis systematically investigates sensor degradation under adverse weather conditions, quantifies its impact on state-of-the-art perception, develops algorithmic enhancements, and integrates weather awareness into the autonomy pipeline.
A LiDAR-only framework for complex outdoor scenes using a factor-graph back-end with reduced drift and improved mapping consistency for dense limited-FoV LiDAR data.
This review examines robust perception under low-visibility conditions as a pipeline-level problem requiring joint consideration of image enhancement, sensor fusion, detection, and evaluation, and indicates a shift from image-quality-oriented restoration toward perception-driven optimization.
Visual SLAM (Simultaneous Localization and Mapping) is a core technology for UAV autonomous navigation. However, it suffers from cumulative errors and heading drift in texture-less environments (e.g., sky, water surfaces) or during longterm operation. Traditional methods relying on GPS or inertial sensors face limitati...
A preprocessing framework for 4D-radar-based 3D object detection that extracts point clouds from radar tensors while preserving object-shape information and suppressing noise and false alarms is proposed.
Traditional simultaneous localization and mapping (SLAM) systems often suffer from feature extraction failures and localization drift when operating in degraded underground coal mine environments characterized by dust interference, low illumination, and homogeneous textures. To address these issues, we propose a robust...
High-level autonomous driving requires an exceptionally accurate and robust perception of surrounding road environments to support reliable vehicle localization. While utilizing multiple LiDAR sensors expands the field of view and provides high-density point clouds, it introduces severe challenges, such as multi-LiDAR...
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